Short Bio

I received my PhD in Computational Mathematics at the University of Padova, where I also held a one year research fellowship year at the Department of Information Engineering. Before joining CERN, I was part the HPC/Cloud group at Leonardo Labs, where I was involved in the design and implementation of cloud stacks for Big Data analytics. Previously, I have worked on the massively parallel solution on GPUs of linear systems arising from differential equations such as high-fidelity numerical simulations of incompressible unsteady Navier-Stokes equations and BVODE problems. In particular, I have explored the performance attained by the approximate iterative solution on GPUs of sparse triangular linear systems in the context of ILU preconditioning with application to immiscible dual fluid flow simulations. Moreover, I delepoped a direct GPU solver taylored for structured matrices arising e.g. from the discretization of two point boundary value problems. I have also dealt with the efficient solution of ill-posed problems my means of low-rank models and statistical techniques with application to analysis of datasets in high dimensional spaces. I developed a block pivoting technique which can be adopted to increase performance in rank deficient QR computations and in sparse recovery problems.

For further information please check out my cv.

Teaching

MOOC: Scientific Computing in Python (in Italian)
Introduction to Python ( Italian version )

Software

LHDM - Active-set solver for NonNegative Least Squares based on Deviation Maximization column selection
QRDM - QR factorization for rank-deficient matrices based on Deviation Maximization column selection
dCATCH - Nearly-optimal polynomial regression designs on high-dimentional point clouds
PARASOF - Direct GPU solver for linear systems with BABD structure
ns2div - Hybrid CPU-GPU solver for Navier-Stokes equations with variable density

Publications

Journal Papers

PhD thesis